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Robust Statistical Analysis on Streaming Data with Near-Duplicates in General Metric Spaces

Summary: Robust streaming statistics for near-duplicates in general metric spaces; sublinear-space algorithms for distinct elements and l0-sampling, with no pre-deduplication. Lower bounds imply noisy general-metric distinct elements are harder than noiseless or Euclidean-noisy settings. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
2014
Venue
PODS
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,651 | 26.93%
DOI
10.1145/3725248

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Authors

BibTeX Citation

@inproceedings{zhang_pods25,
        address = {New York, NY, USA},
        series = {{PODS} '25},
        title = {{Robust Statistical Analysis on Streaming Data with Near-Duplicates in General Metric Spaces}},
        url = {https://dl.acm.org/doi/10.1145/3725248},
        doi = {10.1145/3725248},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Zhang, Qin},
        year = {2025}
}

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Rank Citing Paper Year Venue Pagerank
10,156 Frequency Moments in Noisy Streaming and Distributed Data under Mismatch Ambiguity 2026 PODS 5.093636e-05
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